IMPORTANCE: Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Various deep learning models have been developed for different healthcare predictive tasks using Electronic Health Records and have shown promising performance. In these models, medical codes are often aggregated into visit representation without con...
BACKGROUND: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated.
This study uses video and a pretrained deep convolutional neural network to analyze facial photoplethysmographic signals in detection of atrial fibrillation.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2020
OBJECTIVE: We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured...
PURPOSE: To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging reporting and data system (LI-RADS) (version 2014).
IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to autom...
BACKGROUND: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonst...
AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.
INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manu...
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